More recently, there has been a surge in research at the intersection of causal discovery and federated learning. For instance, DS-FCD [1]} is a gradient-based method where at each round, clients learn an adjacency matrix to approximate the causal graph and neural network representation to approximate the causal mechanisms based on their local data. Another related work is developed by [2]}, where they convert a centralized optimization problem – earlier proposed by [3]} - to a distributed one and solve it through inter-client collaboration. DARLS [4]} is also another method that counts on generalized linear models and distributed optimization to find an optimal DAG without violating clients' privacy. These methods are mostly limited to discovery of DAGS for linear causal mechanism. Furthermore, they only consider horizontal distribution of data, and do not exploit the rich information on interventional samples.
